How to divide NumPy array with zero divisor

A widespread problem in dividing one array of data by another array of data is a zero in divisor. It can happen due to many reasons, for example not very good, or missed data, very small, almost zero divisor data, or any other reasons.


import numpy as np

a = np.array([10.,20.,30.,40.])
b = np.array([2., 3., 0., 4.])
c = a/b
print(c)
# [5.         6.66666667        inf]
#/tmp/ipykernel_786294/546833440.py:2: RuntimeWarning: 
# divide by zero encountered in true_divide c = a/b

Infinity

When you divide something by zero, you will have infinity or undefined results, but infinity is a more physically correct value. By default, NumPy uses inf as a result of dividing by zero and throwing a warning to the output. If you are happy with these results, you can suppress the warning from the zero divisor by following the command:


np.seterr(divide='ignore')

Fixed results or limits

If you know that the results should be fixed, then you can use the NumPy divide function as follows:


c = np.divide(a,b,
              out=np.ones_like(a),
              where=b!=0.0)
print(c)
#[5.         6.66666667 1.        ]

In this code, we divide NumPy array a by b. With the results we will fill array out with is equal in our case one-filled array like a, and this division will be performed only for those elements, where respective divisor b value is not equal 0.0.

Or, otherwise, where the divisor b value is equal to 0.0, the division will not be performed and the respective array element will not be changed (it will be 1.0).

Or you can create an array for filling with zero by command out=np.zeroes_like(a)

How to divide NumPy array with zero divisor

Infinity

Fixed results or limits

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	My Coding > Programming language > Python > Python libraries and packages > Python NumPy > How to divide NumPy array with zero divisor How to divide NumPy array with zero divisor A widespread problem in dividing one array of data by another array of data is a zero in divisor. It can happen due to many reasons, for example not very good, or missed data, very small, almost zero divisor data, or any other reasons. `import numpy as np a = np.array([10.,20.,30.,40.]) b = np.array([2., 3., 0., 4.]) c = a/b print(c) # [5. 6.66666667 inf] #/tmp/ipykernel_786294/546833440.py:2: RuntimeWarning: # divide by zero encountered in true_divide c = a/b` This problem can be handled in NumPy in two different ways. Infinity When you divide something by zero, you will have infinity or undefined results, but infinity is a more physically correct value. By default, NumPy uses inf as a result of dividing by zero and throwing a warning to the output. If you are happy with these results, you can suppress the warning from the zero divisor by following the command: `np.seterr(divide='ignore')` Fixed results or limits If you know that the results should be fixed, then you can use the NumPy divide function as follows: `c = np.divide(a,b, out=np.ones_like(a), where=b!=0.0) print(c) #[5. 6.66666667 1. ]` In this code, we divide NumPy array a by b. With the results we will fill array out with is equal in our case one-filled array like a, and this division will be performed only for those elements, where respective divisor b value is not equal 0.0. Or, otherwise, where the divisor b value is equal to 0.0, the division will not be performed and the respective array element will not be changed (it will be 1.0). Or you can create an array for filling with zero by command out=np.zeroes_like(a) Published: 2023-10-10 00:53:36 Updated: 2023-10-10 00:55:28	Last 10 artitles 2023-12-25 01:48:38 - In-place and out-place Numpy functions 2023-12-25 00:58:08 - First order ordinary differential equations 2023-12-25 00:22:15 - How to increase swap space in Ubuntu 2023-12-24 04:03:12 - How to accelerate math calculations with Numba and Cython 2023-12-19 00:38:20 - 3 ways of function caching in Python 2023-12-19 00:16:55 - Accelerating Python 2023-12-16 12:26:55 - BioPython 2023-12-02 11:05:34 - Reading FITS files with astropy 2023-12-02 05:23:42 - Astropy 2023-12-01 00:33:16 - Circle length calculation with the Monte-Carlo method 9 popular artitles Python Panda (3479) Python NumPy (3011) Python DICTionary HowTo (1608) Python LIST HowTo (1487) Python (1425) Python Generators (1291) Python: How to make absolute links in BeautifulSoup (1098) Programming language (974) NumPy: What the difference between mgrid and meshgrid (956)
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